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14.1 A 126.1mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses.

, , , , , and . ISSCC, page 254-255. IEEE, (2016)

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An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks., , , , and . COOL Chips, page 1-2. IEEE Computer Society, (2017)A 3.13nJ/sample energy-efficient speech extraction processor for robust speech recognition in mobile head-mounted display systems., , , and . ISCAS, page 1790-1793. IEEE, (2015)A 0.53mW ultra-low-power 3D face frontalization processor for face recognition with human-level accuracy in wearable devices., , , , and . ISCAS, page 1-4. IEEE, (2017)A 21mW low-power recurrent neural network accelerator with quantization tables for embedded deep learning applications., , and . A-SSCC, page 237-240. IEEE, (2017)14.2 DNPU: An 8.1TOPS/W reconfigurable CNN-RNN processor for general-purpose deep neural networks., , , and . ISSCC, page 240-241. IEEE, (2017)A 141.4 mW Low-Power Online Deep Neural Network Training Processor for Real-time Object Tracking in Mobile Devices., , , , and . ISCAS, page 1-5. IEEE, (2018)An Energy-Efficient Speech-Extraction Processor for Robust User Speech Recognition in Mobile Head-Mounted Display Systems., , , and . IEEE Trans. Circuits Syst. II Express Briefs, 64-II (4): 457-461 (2017)Talaria: Interactively Optimizing Machine Learning Models for Efficient Inference., , , , , , , , , and . CoRR, (2024)LNPU: A 25.3TFLOPS/W Sparse Deep-Neural-Network Learning Processor with Fine-Grained Mixed Precision of FP8-FP16., , , , , and . ISSCC, page 142-144. IEEE, (2019)An 8.3mW 1.6Msamples/s multi-modal event-driven speech enhancement processor for robust speech recognition in smart glasses., , , and . ESSCIRC, page 117-120. IEEE, (2016)